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Worldwide use phase electricity consumption of personal computers. Desktops still dominate the result. The shift to more energy-efficient technologies has tempered overall electricity use: while the number of desktops+laptops has grown at 10.9% per year from 2007 to 2012, the electricity consumption has only grown at 5.1% per year. The circular markers • indicate years for which the subcategory results are (mainly) based on data for that specific year; non-marked data points are (mainly) interpolations or extrapolations.
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Information and Communication Technology (ICT) devices and services are becoming more and more widespread in all aspects of human life. Following an increased worldwide focus on the environmental impacts of energy consumption in general, there is also a growing attention to the electricity consumption associated with ICT equipment.
In this paper we...
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Context 1
... final results of our calculations are given in Table 2 and shown in Fig. 3. The total energy consumption by personal com- puters and their displays is currently around 300 TWh per year. The annual growth rate of this total electricity consumption over the time frame 2006 to 2012 is 5.3%. This growth rate is signif- icantly lower than for device numbers (which is around 11-12% for computers and monitors), ...
Context 2
... this power- efficiency trend leads to the emergence of new technologies such as laptops and mobile phones. These, in turn, have led to new services and applications leading to an overall increase in ICT power consumption. In this light, it is interesting to observe that our results do not indicate (yet) that laptops have replaced desktops (in Fig. 3 there is no decline in desktop power con- sumption); laptops appear to be used in addition to the already existing worldwide user base of ...
Context 3
... electricity use from 2011 to 2020 9 . The reason behind this downward trend is the halving of the average electricity use per device by 2020, driven by both 'efficiency gains and fewer hours spent on PCs due to the emergence of smart devices, and a greater use of lap- tops vs. desktops' [6]. We can not yet observe this trend in our estimates, see Fig. ...
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... This trend may become unmanageable, requiring lower data center energy utilization during periods of low demand, enhancing core router energy efficiency, and implementing more energy-efficient admittance network technology. Van Heddeghem et al. (2014) conducted a global analysis of ICT electricity usage, estimating that infrastructure and digital gadgets are consuming more power than the world's total electricity consumption. They found that data centres, personal computers, and communication networks have grown annually at rates of 10, 5, and 4 percent, respectively. ...
The data-driven economy is transforming with data centers becoming a crucial business infrastructure. However, the increasing reliance on data centers is posing a threat to the environment. Climate change activists are focusing on reducing emissions from sectors like automotive, aviation, and energy. Data centers consume more electricity than the UK, accounting for 3% of global electricity supply and 2% of total greenhouse gas emissions. By 2040, digital data storage is projected to contribute to 14% of the world's emissions. The number of data centers worldwide has surged from 500,000 in 2012 to over 8 million, with energy consumption doubling every four years. The rise in internet penetration rates and the introduction of 5G technologies and IoT devices will further exacerbate the issue, increasing the demand for data processing.
... For instance, researchers demonstrate that there are certain contradictions between smart digital cities and sustainable development goals (Martin et al., 2018), note that digitalization is not yet proved to be essential for reducing energy consumption and greenhouse gas emissions (Jin et al., 2018), and assume that digital equipment causes a lot of damage to the environment during production, maintenance and disposal (Kuntsman and Rattle, 2019). The main argument for the inverse relationship between the digital economy and reducing the burden on the environment is the fact that the use of digital technologies (big data, in particular) increases energy consumption (Van Heddeghem et al., 2014;Zhou et al., 2018). The researchers claim that the share of digital infrastructure in the national energy consumption can reach up to 10%-15%. ...
Sustainable development and ecological restoration are a common goal pursued by countries around the world to mitigate the collision between economic growth and the environment. Digital economy has been rather instrumental in settling this type of conflict. The study is intended to identify the relationship between digital financing and environmental financing by assessing the specificities of their temporal and industry-specific dynamics, as well as to determine the side effects that the digital economy has in terms of current environmental investments and costs. The special attention is paid to the effect of the digital economy on both total environmental financing and its components, namely, environmental investment and current environmental protection costs. The authors come up with two indicators to evaluate the impact of the digital economy, these are digital financing (direct impact) and digital capital (indirect impact). To calculate these indicators, the authors’ own method is developed. The impact of the digital economy on environmental financing was tested using the least squares method with clustering of annual standard deviation and individual fixed effects. The research data were retrieved from the Federal State Statistics Service (Rosstat) of the Russian Federation for 2012–2022. Our findings show that digital financing exerts a significant positive effect on environmental financing, which indicates that two dynamic processes in the economy—digital transformation and introduction of advanced environmental digital technologies—are synchronized. The authors prove that digital investments stimulate a comparable increase in environmental investment due to the effects created by digital technologies penetrating into environmental protection technologies. We demonstrate that the level of digitalization of the population, companies and the state assessed through the digital capital index has a positive effect on environmental financing. The results of the study are of use in the sphere of public policy.
... The increase in internet access also has an impact on the high and low consumption of electrical energy. According to (Van Heddeghem et al., 2014) communication networks, including cellular, fixed broadband and telephone networks, consumed 1.7% of total global electricity usage in 2012. ...
This study aims to analyze the determinants of variables that affect electricity consumption in Indonesia 2011-2020. There are 4 variables that are used as determinant variables, namely (1) economic growth, (2) population growth, (3) Human Development Index (HDI), and (4) internet access. This study uses a random effect model regression method using data that is panel data consisting of 34 provinces in Indonesia in the span of 2011 - 2020. Secondary data were obtained from the Central Statistics Agency (BPS), a statistical report of PT. PLN (Persero) and previous research literature studies. The results of this study found that (1) the variable of economic growth did not affect electricity consumption, (2) the population growth variable also did not affect the consumption of electricity, (3) the variable Human Development Index (HDI) had a positive effect on electricity consumption and (4) internet access variable has a negative effect on electricity consumption. Suggestions that the author can convey are: (1) optimizing the potential of New Renewable Energy (EBT), (2) conserving electricity and making efficient use of electricity, (3) increasing the electrification ratio so that electricity can be enjoyed by the entire community.
... In this regard, the power usage of data centers in the USA was about 1.4% and 1.8% of all electricity used in all of the USA in the years 2010 and 2014, respectively [8][9][10]. Furthermore, another prediction says that the energy consumption of IT departments will rise up to 13% by 2030 [11]. Therefore, inefficient task scheduling schemes on a larger scale are problematic. ...
Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user’s quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders’ requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems.
... From electricity consumption to carbon emissions, the proportion of thermal electricity generation is likely to change because of the digital service trade. Unlike previous studies that only consider the change in electricity consumption of digital infrastructure (e.g., Heddeghem et al. 2014;Gnangnon 2020), this study takes both electricity consumption and the proportion of thermal electricity generation into account when measuring carbon emissions from digital infrastructure. ...
... However, there are also some studies claiming that the digital service trade may also increase emissions (Heddeghem et al. 2014;Toader et al. 2018). These studies mainly focus on emissions from digital infrastructure, which is fundamental to digital service trade (Wolfe 2019). ...
... That is, although digital service trade infrastructure increases electricity consumption (thus increasing carbon emissions), it also reduces the proportion of thermal electricity generation (thus reducing carbon emissions). However, previous studies have ignored the change of the latter (e.g., Heddeghem et al. 2014;Gnangnon 2020). On the other hand, they fail to employ the standard framework to empirically test whether this mediator is established (e.g., Qu et al. 2022). ...
This study explores the nonlinear relationship between digital service trade and carbon emissions using the data of 42 G20 countries from 2005 to 2021. It is concluded that this relationship appears to be an inverted U-shape. When the digital service trade is inadequate, this exacerbates carbon emissions. Only when the digital service trade is above this point does it become beneficial to environmentally sustainable development. Mediation analyses indicate that the digital service trade increases carbon emissions by increasing emissions from digital infrastructure and decreases them by optimizing the industrial structure and adopting low-carbon technologies. Furthermore, the nonlinear association is moderated by human capital. When human capital is insufficient, it is harder for the digital service trade to curb carbon emissions. Accordingly, this study is helpful to develop the digital service trade and achieving the goals of carbon peak and neutralization and the sustainable growth of the environment.
... T HE progressive development of fiber networks is a driver of productivity growth in modern societies [1]. As a result, network electricity consumption has been estimated as ≈ 1.7% of the global consumption, with a growth rate of ≈ 10% per year [2] in 2012. Thus, energy efficiency has become a key network design principle and one of the main motivations to switch from electrical to optical informationprocessing in networks, e.g., via the introduction of integratedphotonics circuits [3]. ...
... Since this does not mark any qualitative change in our results, we restrict to equal spacing for simplicity. 2 Throughout the main text, except in Results III-C, we set ω = 1 for some fixed signal frequency ω, effectively equating the mean energy of the field with its mean number of photons. < l a t e x i t s h a 1 _ b a s e 6 4 = " u J H n c g l s S 2 7 c I X I Y H a W 7 t l w X y 1 A = " > A A A D 0 3 i c b V J L b 9 N A E N 7 G P E p 4 t X D k s s J C K h K K Y q u C H s t D g g N C R W r a S r E V r T e T d J X d t b U 7 b l N t c 0 F c O X K F / 8 C / 4 d + w N g n U T u c 0 / h 7 7 j X c 2 K 6 S w 2 O / / 3 u g E N 2 7 e u r 1 ...
... Thus for every choice of N, τ and ν the dependence of the channel capacity on the baud-rate is effectively logarithmic and grows unbounded as τ · N · log( b τ ·N +ν ) when using an OJDR. In sharp contrast, the capacity of the same channel using an OSSR is limited by τ ·N ln (2) . From a theoretical perspective, our analysis suggests that a scaling of capacity with baud-rate is possible even in the presence of amplifiers, when quantum information processing technology is used. ...
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$\sim 57$
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... However, from the consumer perspective, there are also different viewpoints, as DT may lead to an expansion of consumer product demand, resulting in increased production scale by businesses and generating a rebound effect (Lange et al., 2020). In such cases, although the energy efficiency per unit of product improves, the overall energy consumption increases (Heddeghem et al., 2014). Preventing the energy rebound effects of DT has become an important task that cannot be ignored in the process of achieving carbon neutrality. ...
Introduction: Improving energy efficiency is significant for achieving carbon emission reduction and promoting the transformation of green economic development. In the sustainable development framework set out in the 2030 Agenda for Sustainable Development, Goal 7.3 explicitly aims to double the global rate of energy efficiency improvement by 2030. The rapid development of digital technology, along with its universality and penetrative characteristics, has provide a feasible solution for improving energy efficiency and environmental conditions. However, the theoretical understanding of the impact and underlying logic of digital technology on energy efficiency remains unclear.
Methods: Based on the panel data of 30 provinces in China from 2006 to 2021, this paper adopts econometric methods, including two-way fixed effect, instrumental variable method, and Driscoll-Kraay standard error. It investigates the influence of digital technology on energy efficiency and its internal mechanism from single factor and all factor levels.
Result: The results show that Digital technology, represented by industrial robots, significantly improves energy efficiency, whether measured by the energy consumption intensity of GDP or the total-factor energy efficiency estimated using the SBM-GML model. The results still hold even after conducting endogeneity tests and robustness tests. Digital technology can improve energy efficiency by increasing virtual industrial agglomeration and promoting outward foreign direct investment.
Discussion: In addition to promoting the theoretical understanding of the impact of digital technology on energy efficiency and exploring its mechanism, this paper also provides empirical evidence for policy makers and enterprises to formulate effective measures and strategies to improve energy efficiency under the background of digital economy.
... Further, Salahuddin and Gow (2016) showed that an increase in Internet use also leads the economic growth in South Africa. Although Heddeghem et al. (2014) argued that 3.9 to 4.6% of energy consumption was increased due to the product and services of Internet use. Later on, Belkhir and Elmeligi (2018) analyzed that Internet use increaseselectricity use, which ultimately influences the emissions of CO 2 . ...
ICTs and access to Internet use are considered vital for the achievement of sustainable development goals. So, this study explored the effect of the global digital divide, trade openness, renewable energy consumption, and forestation on greenhouse gas (GHG) emissions in 42 high-income countries (HICs) and high-middle-income (HMICs), low-income countries (LICs), and low-middle-income countries (LMICs) of Africa from 1990 to 2018. TheDumitrescu-Hurlin causality results confirmed a unidirectional causality from GHG emissions to the global digital divide (HICs and HMICs), global digital divide to GHG emissions (LICs), and GHG emission to trade openness (LICs and LMICs). Moreover, the long-run results of the autoregressive distributed lag (ARDL) model showed an increase in GHG due to an increase in the global digital divide in all three panels. Further, ARDL results showed reduced GHG emissions due to increased trade openness in LIC and LMICs, renewable energy consumption, and forestation in all three panels. Thus, to encounter pollution from Internet use, the government should start environment-friendly projects through public and private investment in smart and modern environment-friendly technology and reduce the taxes and tariffs on them. Moreover, the governments of African countries should create public awareness through print and electronic media for raising the forestation area.
... The increased power consumption is partly a result of the strong relationship between ICT technology and daily living (Røpke et al., 2010;Zheng et al., 2020). Van Heddeghem et al. (2014) found yearly growth rates of 4%, 5%, and 10% for data centers, personal computers, and communication networks, respectively. These rates are higher than the growth in world electricity usage. ...
The growth of the internet has a significant impact on the development of energy-saving technology, increased energy efficiency, and decreased power intensity. This study applies the spatial econometric model and the panel threshold model to examine the relationship between Internet development, economic growth, and power intensity in Henan Province using data from 18 cities in the province from 2001 to 2018. The findings indicate that the power intensity in Henan Province lowers as Internet usage rises and the industrial structure is optimized. Additionally, Henan Province's urban power density exhibits spatial positive correlation and features of regional aggregation. Finally, Henan Province has experienced a substantial threshold effect as a result of Internet development. The level of Internet development has a non-linear impact on power intensity at various levels of population, per capita GDP, urbanization rate, technological development, and foreign direct investment.
... In recent years, wide adoption of ICT and exponential growth of Internet users have significant contribution in increases of world energy consumption [32], and impact of digital economy is expected to increase more over next year's [32,33]. The largescale CDC consumes a large amount of electrical energy for operations and cooling purposes. ...
... In recent years, wide adoption of ICT and exponential growth of Internet users have significant contribution in increases of world energy consumption [32], and impact of digital economy is expected to increase more over next year's [32,33]. The largescale CDC consumes a large amount of electrical energy for operations and cooling purposes. ...
In Cloud Data Centers (CDC), dynamic consolidation of Virtual Machines (VMs) is an efficient technique to increase resource usage and energy efficiency. Energy efficiency has become a new frontier in the virtualized cloud computing paradigm. Data centers must decide how, which, and when VMs must be merged onto a few physical servers, which is a difficult task. Server consolidation necessitates VMs migration, which has a direct influence on service response time. It means that overloading of server results in resource scarcity and application performance deterioration. Most of the existing solutions for VMs consolidation are based on the heuristic methods. The drawbacks of these methods are they produce sub-optimal outcomes and prevent the precise description of a Quality of Service (QoS) target. Further, the requirement for energy efficiency has grown as the number and scale of CDC has grown. Virtual machine migration is widely utilized cloud computing technology that is thus focused in this work to conserve energy. We conduct an in-depth study of the existing literature on that address server consolidation and energy consumption issues and provide optimal solutions. We conclude the survey by describing key challenges for future research on constructing effective and accurate server consolidation techniques with power modeling.
